Biology subjects

Little, S. J.

Publications and source records attributed to Little, S. J..

6 recordsLinked to original sources

bioRxiv2021

Rapid dynamic naturalistic monitoring of bradykinesia in Parkinson's disease using a wrist-worn accelerometer

IntroductionMotor fluctuations in Parkinsons disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefit on symptoms including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinsons patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. Here, we investigate the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinsons patients using a single wrist-accelerometer. MethodsAs part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist-accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the whole one-hour recordings, and medication-state classification analyses were performed on one-minute segments of data. The influence of individual versus group model training, data window length, and total amount of training patients included in group model training on classification was analyzed. ResultsStatistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, +/- respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 seconds, and with additional training patient datasets. ConclusionMedication induced fluctuations in bradykinesia can be classified using wrist worn accelerometery at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has important clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.

neuroscience↗

bioRxiv2021

Analysis-rcs-data: Open-source toolbox for the ingestion, time-alignment, and visualization of sense and stimulation data from the Medtronic Summit RC+S system

Closed-loop neurostimulation is a promising therapy being tested and clinically implemented in a growing number of neurological and psychiatric indications. This therapy is enabled by chronically implanted, bidirectional devices including the Medtronic Summit RC+S system. In order to successfully optimize therapy for patients implanted with these devices, analyses must be conducted offline on the recorded neural data, in order to inform optimal sense and stimulation parameters. The file format, volume, and complexity of raw data from these device necessitate conversion, parsing, and time reconstruction ahead of time-frequency analyses and modeling common to standard neuroscientific analyses. Here, we provide an open-source toolbox written in Matlab which takes raw files from the Summit RC+S and transforms these data into a standardized format amenable to conventional analyses. Furthermore, we provide a plotting tool which can aid in the visualization of multiple data streams and sense, stimulation, and therapy settings. Finally, we describe an analysis module which replicates RC+S on-board power computations, functionality which can accelerate biomarker discovery. This toolbox aims to accelerate the research and clinical advances made possible by longitudinal neural recordings and adaptive neurostimulation in people with neurological and psychiatric illnesses.

neuroscience↗

bioRxiv2021

MrHAMER yields highly accurate single molecule viral sequences enabling analysis of intra-host evolution

Technical challenges remain in the sequencing of RNA viruses due to their high intra-host diversity. This bottleneck is particularly pronounced when interrogating long-range co-evolution given the read-length limitations of next-generation sequencing platforms. This has hampered the direct observation of long-range genetic interactions that code for protein-protein interfaces with relevance in both drug and vaccine development. Here we overcome these technical limitations by developing a nanopore-based long-range viral sequencing pipeline that yields accurate single molecule sequences of circulating virions from clinical samples. We demonstrate its utility in observing the evolution of individual HIV Gag-Pol genomes in response to antiviral pressure. Our pipeline, called Multi-read Hairpin Mediated Error-correction Reaction (MrHAMER), yields >1000s viral genomes per sample at 99.9% accuracy, maintains the original proportion of sequenced virions present in a complex mixture, and allows the detection of rare viral genomes with their associated mutations present at <1% frequency. This method facilitates scalable investigation of genetic correlates of resistance to both antiviral therapy and immune pressure, and enable the identification of novel host-viral and viral-viral interfaces that can be modulated for therapeutic benefit.

genomics↗

bioRxiv2021

The sensitivity of ECG contamination to surgical implantation site in adaptive closed-loop neurostimulation systems

BackgroundBrain sensing devices are approved today for Parkinsons, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability. ObjectivesInvestigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity. MethodsElectric field modelling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination.a ResultsThe impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination. ConclusionsGiven the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for "closed-loop" algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration. HighlightsO_LIChronic embedded brain sensing promises algorithm-based neurostimulation C_LIO_LIAlgorithms for closed-loop stimulation can be impaired by artifacts C_LIO_LIThe relationship of implant location to cardiac dipole has relevant impact on neural signal fidelity; simple models can provide guidance on the sensitivity C_LIO_LIECG artifacts are present in up to 50% of neural signals from left subclavicular DBS systems C_LIO_LIImplanting DBS in a right subclavicular location significantly reduces frequency of ECG artifacts C_LIO_LICranial-mounted implants are relatively immune to artifacts C_LI

neuroscience↗

bioRxiv2018

Quantifying the performance of MEG source reconstruction using resting state data

Resting state networks measured with magnetoencephalography (MEG) form transiently stable spatio-temporal patterns in the subsecond range, and therefore fluctuate more rapidly than previously appreciated. These states populate and interact across the whole brain, are simple to record, and possess the same dynamic structure of task related changes. They therefore provide a generic, heterogeneous, and plentiful functional substrate against which to test different MEG recording and reconstruction approaches. Here we validate a non-invasive method for quantifying the resolution of different inversion assumptions under different recording regimes (with and without head-casts) based on resting state MEG. Spatio-temporally partitioning of data into self-similar periods confirmed a rich and rapidly dynamic temporal structure with a small number of regularly reoccurring states. To test the anatomical precision that could be resolved through these transient states we then inverted these data onto libraries of systematically distorted, subject specific, cortical meshes and compared the quality of the fit using Cross Validation and a Free Energy metric. This revealed which inversion scheme was able to best support the least distorted (most accurate) anatomical models. Both datasets showed an increase in model fit as anatomical models moved towards the true cortical surface. In the head-cast MEG data, the Empirical Bayesian Beamformer (EBB) algorithm showed the best mean anatomical discrimination (3.7 mm) compared with Minimum Norm / LORETA (6.0 mm) and Multiple Sparse priors (9.4 mm). This pattern was replicated in the second (conventional dataset) although with a marginally poorer prediction of the missing (cross-validated) data. Our findings suggest that the abundant resting state data now commonly available could be used to refine and validate MEG source reconstruction methods or recording paradigms.

neuroscience↗

bioRxiv2017

Diagnostic, Infection Timing and Incidence Surveillance Applications of High Dynamic Range Chemiluminescent HIV Immuno-Assay Platforms

BackgroundCustom staging assays, including the Sedia HIV-1 Limiting Antigen Avidity EIA (LAg) and avidity modifications of the Ortho VITROS anti-HIV-1+2 and Abbott ARCHITECT HIV Ag/Ab Combo assays, are used to identify recent infections in clinical settings and for cross-sectional HIV incidence estimation. However, the high dynamic range of chemiluminescent platforms allows differentiating recent and longstanding infection on signal intensity, and this raises the prospect of using unmodified diagnostic assays for infection timing and surveillance applications.\n\nMethodsWe tested a panel of 2,500 well-characterised specimens with estimable duration of HIV infection with the three assays and the unmodified ARCHITECT. Regression models were used to estimate mean durations of recent infection (MDRI), context-specific false-recent rates (FRR) and correlation between signal intensity and LAg measurements. A hypothetical epidemiological scenario was constructed to evaluate utility in surveillance applications.\n\nResultsOver a range of MDRIs (reflecting recency discrimination thresholds), a diluted ARCHITECT-based RITA produced lower FRRs than the VITROS platform (FRR {approx} 0.5% and 1.5% respectively at MDRI of 200 days) and the unmodified diagnostic ARCHITECT produces incidence estimates with comparable precision to LAg (RSE {approx} 17.5% and 15% respectively at MDRI of 200 days). ARCHITECT S/CO measurements were highly correlated with LAg ODn measurements (r = 0.80) and values below 200 are strongly predictive of LAg recency and duration of infection less than one year.\n\nConclusionsLow quantitative measurements from the unmodified ARCHITECT obviate the need for additional recency testing and its use is feasible in clinical staging and incidence surveillance applications.

epidemiology↗